Application of deep learning to predict advanced neoplasia using big clinical data in colorectal cancer screening of asymptomatic adults.

ABSTRACT:

Background/aims

We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adults, based on which colorectal cancer screening could be customized.

Methods

We collected data on 26 clinical and laboratory parameters, including age, sex, smoking status, body mass index, complete blood count, blood chemistry, and tumor marker, from 70,336 first-time colonoscopy screening recipients. For reference, we used a logistic regression (LR) model with nine variables manually selected from the 26 variables. A deep neural network (DNN) model was developed using all 26 variables. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the models were compared in a randomly split v

SUBMITTER: Yang HJ 

PROVIDER: S-EPMC8273821 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature